Active surveillance is a management strategy developed to avoid unnecessary treatment in patients with low-risk prostate cancer. Patients on active surveillance are monitored through PSA kinetics and serial biopsies followed by radical intervention in the case of disease progression. PSA kinetics, biopsies, and nomograms are only modest predictors of adverse pathology following radical-prostatectomy, suggesting a need for novel biomarkers to detect significant disease. The laboratory's long-term goal is to develop novel prognostic biomarkers for prostate cancer patients. The objective here is to discover small, non-coding, single-stranded microRNAs (miRNAs) that predict significant disease in prostate cancer patients who are candidates for active surveillance. The central hypothesis is that serum miRNA signatures detect significant disease in prostate cancer patients classified as low-risk. This hypothesis arises from the preliminary data produced in the applicants' laboratory. The rationale for this project is that discovering accurate predictors of significant disease in low-risk prostate cancer patients will enhance the effectiveness of active surveillance and avoid delay of appropriate treatment when necessary. The hypothesis formulated from the preliminary data will be tested by pursuing two specific aims: 1) Identify candidate miRNAs associated with significant disease in low-risk prostate cancer patients; and 2) Evaluate the ability of candidate miRNAs to predict significant disease in low-risk prostate cancer patients. Under the first aim, a novel multiplex qRT-PCR method utilized to generate preliminary data will be used to characterize miRNA signatures in serum from candidates for active surveillance, who elect to undergo immediate radical prostatectomy instead. MiRNAs having differential expression between patients found to have a Gleason sum of 7 or higher and patients found to have a Gleason sum of 6 or less following surgery will be identified as potential biomarkers for significant disease. Under the second aim, the accuracy of the potential biomarkers will be evaluated by developing a prediction model distinguishing patients found to have a Gleason sum of 7 or higher from patients found to have a Gleason sum of 6 or less following radical prostatectomy after being on active surveillance. The proposed research is significant because it is expected to improve the detection of significant disease in low-risk patients and directly increase the effectiveness of active surveillance as a management strategy for prostate cancer. Ultimately, such improvements will benefit patients by ensuring treatment to patients with significant disease while decreasing morbidities related to radical interventions.